pacman::p_load(dplyr, readr, sf, tidyverse, tmap, sfdep, ggpubr, Metrics, ggplot2, plotly, spdep, rjson, od, gifski, stplanr)
Caution
This article and analysis is a work-in-progress! Please read and interpret results at your own risk, check back for the final article!
1 Overview
1.1 Background
With the introduction of Thomson East Coast Line 3 between Caldecott and Gardens by the Bay stations, bridging the Upper Thomson area towards the city, the Land Transport Authority of Singapore (LTA) sought to reduce duplication of bus routes with new train lines, which was common practice in Singapore.
Yet, the announcement of the discontinuation of bus service 167 was widely
1.2 Objectives
Understand more about the initial failure of the route rationalisation of bus service 167:
- Commuters perspective
- Why a hub-and-spoke approach (with the introduction of Thomson East Coast Line) is insufficient to shift demand? [pending analysis]
Click here to skip to the analysis
2 Getting Started
2.1 Setting Up
Packages required to be loaded for
2.2 Data Sources
| Dataset Name | Source | Methodology |
|---|---|---|
| Origin-Destination Passenger Count for Buses (OD) Nov 2023 | LTA Datamall | API |
| Bus Routes as of 26 Nov 2023 | LTA Datamall | API |
| Bus Stops as of 26 Nov 2023 | LTA Datamall | API |
3 Data Preparation
3.1 Loading Data
Loading the Origin-Destination Passenger Count for Buses
OD_2023_11 <- read.csv("data/167_OD_analysis/origin_destination_bus_202311.csv")Loading the Bus Routes JSON file:
BUS_ROUTE <- fromJSON(file="data/167_OD_analysis/busroute_2023-11-26.json")Loading the Bus Stops JSON file:
BUS_STOP <- fromJSON(file="data/167_OD_analysis/busstop_2023-11-26.json")Loading the Bus Service JSON file:
BUS_SERVICE <- fromJSON(file="data/167_OD_analysis/busservice_2023-11-26.json")Load MPSZ (2019):
mpsz <- st_read(dsn = "data/167_OD_analysis/",
layer = "MPSZ-2019") %>%
st_transform(crs = 3414)Reading layer `MPSZ-2019' from data source
`C:\Users\boylu\OneDrive - Singapore Management University\0_git-projects\urbancoalesce\explore\data\167_OD_analysis'
using driver `ESRI Shapefile'
Simple feature collection with 332 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 103.6057 ymin: 1.158699 xmax: 104.0885 ymax: 1.470775
Geodetic CRS: WGS 843.2 Define Static Variables
3.2.1 Information of Month
WEEKDAY_OF_MONTH = 21
SUN_PH_OF_MONTH = 5
SAT_OF_MONTH = 4
WEEKEND_OF_MONTH = SUN_PH_OF_MONTH + SAT_OF_MONTH3.2.2 Normalise Data
As the OD data from LTA sums trips from entire month, we need to normalise them to trips per day for ease of comparison between Weekdays, Saturdays and Sun_PH.
OD_2023_11 <- OD_2023_11 %>% mutate(TOTAL_TRIPS = ifelse(DAY_TYPE=="WEEKENDS/HOLIDAY", TOTAL_TRIPS / WEEKEND_OF_MONTH, TOTAL_TRIPS / WEEKDAY_OF_MONTH ))3.3 Extracting Relevant Information
For the OD Passenger Count, we are only interested in obtaining counts which involves bus service 167. We will need to extract it twice, once for each direction.
We are not implementing a check for stops since the JSON data from LTA Datamall is returned in stop sequence.
3.3.1 Extract 167 Bus Stops
Note
As LTA’s OD Count stores CBD area bus stops starting with 0 as 4 digit codes instead of 5 digit prefixed with 0, we recode the bus stops as numeric and drop the ‘0’ prefix
BS_167_DIR_1_DF <- data.frame(Seq = integer(), BS_Code = integer())
BS_167_DIR_2_DF <- data.frame(Seq = integer(), BS_Code = integer())
for (route_info in BUS_ROUTE){
if (route_info$ServiceNo == "167"){
if (route_info$Direction == 1){
BS_167_DIR_1_temp <- data.frame(Seq = as.numeric(route_info$StopSequence), BS_Code = as.numeric(route_info$BusStopCode))
BS_167_DIR_1_DF[nrow(BS_167_DIR_1_DF) +1,] <- BS_167_DIR_1_temp
}
else if (route_info$Direction == 2){
BS_167_DIR_2_temp <- data.frame(Seq = as.numeric(route_info$StopSequence), BS_Code = as.numeric(route_info$BusStopCode))
BS_167_DIR_2_DF[nrow(BS_167_DIR_2_DF) +1,] <- BS_167_DIR_2_temp
}
}
}
rm(BS_167_DIR_1_temp)
rm(BS_167_DIR_2_temp)3.3.2 Append Bus Stop Names to DataFrame
We convert the List format of Bus Stops to a more workable DataFrame format
BUS_STOP_DF <- data.frame(BS_Code = integer(), BS_Road_Name = character(), BS_Name = character(), Latitude = double(), Longitude = double())
for (bs in BUS_STOP){
BS_TEMP <- data.frame(BS_Code = as.numeric(bs$BusStopCode), BS_Road_Name = bs$RoadName, BS_Name = bs$Description, Latitude = as.numeric(bs$Latitude), Longitude = as.numeric(bs$Longitude))
BUS_STOP_DF[nrow(BUS_STOP_DF) +1,] <- BS_TEMP
}
rm(BS_TEMP)We then do a left join, merging the bus stop info into Bus Service direction DataFrames
BS_167_DIR_1_DF <- merge(x=BS_167_DIR_1_DF,y=BUS_STOP_DF,
by="BS_Code", all.x=TRUE)
BS_167_DIR_2_DF <- merge(x=BS_167_DIR_2_DF,y=BUS_STOP_DF,
by="BS_Code", all.x=TRUE)3.3.3 Reset Row Index Numbering
rownames(BS_167_DIR_1_DF) <- BS_167_DIR_1_DF$Seq
rownames(BS_167_DIR_2_DF) <- BS_167_DIR_2_DF$Seq3.3.5 Extract Bus Route Info to DataFrame
We save information that is required for our analysis from JSON to DataFrame format
BUS_ROUTE_DF <- data.frame(Service_No = character(), Direction = integer(), Seq = integer(), BS_Code = integer(), Distance = double(), WD_FirstBus = integer(), WD_LastBus = integer(), SAT_FirstBus = integer(), SAT_LastBus = integer(), SUN_FirstBus = integer(), SUN_LastBus = integer())
for (route in BUS_ROUTE){
BS_RT_TEMP <- data.frame(ServiceNo = route$ServiceNo, Direction = as.numeric(route$Direction), Seq = as.numeric(route$StopSequence), BS_Code = as.numeric(route$BusStopCode), Distance = as.numeric(route$Distance), WD_FirstBus = as.numeric(route$WD_FirstBus), WD_LastBus = as.numeric(route$WD_LastBus), SAT_FirstBus = as.numeric(route$SAT_FirstBus), SAT_LastBus = as.numeric(route$SAT_LastBus), SUN_FirstBus = as.numeric(route$SUN_FirstBus), SUN_LastBus = as.numeric(route$SUN_LastBus))
BUS_ROUTE_DF[nrow(BUS_ROUTE_DF) +1,] <- BS_RT_TEMP
}
rm(BS_RT_TEMP)3.3.6 Extract Bus Service Info to DataFrame
BUS_SVC_DF <- data.frame(Service_No = character(), Direction = integer(), Category = character(), AM_Peak_Freq = character(), AM_OffPeak_Freq = character(), PM_Peak_Freq = character(), PM_OffPeak_Freq = character(), LoopDesc = character())
for (svc in BUS_SERVICE){
BS_SVC_TEMP <- data.frame(Service_No = svc$ServiceNo, Direction = as.numeric(svc$Direction), Category = svc$Category, AM_Peak_Freq = svc$AM_Peak_Freq, AM_OffPeak_Freq = svc$AM_Offpeak_Freq, PM_OffPeak_Freq = svc$PM_Offpeak_Freq, PM_Peak_Freq = svc$PM_Peak_Freq, LoopDesc = svc$LoopDesc)
BUS_SVC_DF[nrow(BUS_SVC_DF) +1,] <- BS_SVC_TEMP
}
rm(BS_SVC_TEMP)3.3.7 Discard Unneeded Variables
rm(BUS_ROUTE)
rm(BUS_SERVICE)
rm(route)
rm(svc)
rm(bs)
rm(route_info)4 Exploratory Data Analysis
Investigating the Bus Stops on Bus Service 167
71 Stops
BS_167_DIR_1_DF[order(BS_167_DIR_1_DF$Seq),] BS_Code Seq BS_Road_Name BS_Name Latitude Longitude
1 58009 1 Sembawang Vista Sembawang Int 1.447482 103.8194
2 58151 2 Canberra Rd Bef Sembawang Stn 1.448359 103.8220
3 58331 3 Canberra Link Blk 589D 1.449741 103.8240
4 58039 4 Sembawang Rd Bef Canberra Dr 1.446742 103.8258
5 58029 5 Sembawang Rd The Nautical 1.443159 103.8240
6 58019 6 Sembawang Rd Aft Sembawang Shop Ctr 1.440850 103.8247
7 57139 7 Sembawang Rd Aft Jln Kemuning 1.437957 103.8256
8 57129 8 Sembawang Rd Blk 114 1.434187 103.8264
9 57119 9 Sembawang Rd Blk 101 1.431303 103.8269
10 57089 10 Sembawang Rd Blk 713 1.427405 103.8269
11 57079 11 Sembawang Rd Khatib Camp 1.424439 103.8257
12 57069 12 Sembawang Rd Opp Dieppe Barracks 1.418966 103.8246
13 57059 13 Sembawang Rd Opp Sembawang Air Base 1.414728 103.8236
14 57049 14 Sembawang Rd Opp Nee Soon HQ 22 SIB 1.410820 103.8223
15 57039 15 Sembawang Rd SPIRITUAL GRACE PRESBY CH 1.406118 103.8200
16 57029 16 Sembawang Rd Aft The Springside 1.403963 103.8186
17 57019 17 Upp Thomson Rd Springleaf Nature Pk 1.400563 103.8173
18 56099 18 Upp Thomson Rd Springleaf Stn Exit 2 1.396068 103.8188
19 56089 19 Upp Thomson Rd Aft SLE 1.392051 103.8186
20 56079 20 Upp Thomson Rd Aft Old Upp Thomson Rd 1.387566 103.8196
21 56069 21 Upp Thomson Rd Bef Tagore Dr 1.385305 103.8232
22 56059 22 Upp Thomson Rd Bef Tagore Rd 1.382688 103.8260
23 56049 23 Upp Thomson Rd Meadows @ Peirce 1.379456 103.8277
24 56039 24 Upp Thomson Rd Aft Yio Chu Kang Rd 1.376731 103.8285
25 56029 25 Upp Thomson Rd Bef Sembawang Hills Fc 1.373595 103.8287
26 56019 26 Upp Thomson Rd Bef Ang Mo Kio Ave 1 1.369486 103.8286
27 53099 27 Upp Thomson Rd Aft Ang Mo Kio Ave 1 1.366606 103.8284
28 53089 28 Upp Thomson Rd Faber Gdn 1.363977 103.8282
29 53079 29 Upp Thomson Rd Flame Tree Pk 1.360827 103.8286
30 53069 30 Upp Thomson Rd Aft Windsor Pk Rd 1.357772 103.8289
31 53059 31 Upp Thomson Rd Upp Thomson Stn Exit 2 1.355471 103.8312
32 53049 32 Upp Thomson Rd Bef Jln Todak 1.353627 103.8342
33 53039 33 Upp Thomson Rd Thomson CC 1.351447 103.8359
34 53029 34 Upp Thomson Rd Shunfu Est 1.349392 103.8373
35 53019 35 Upp Thomson Rd OPP ST. THERESA'S HME 1.346003 103.8387
36 51069 36 Thomson Rd Mt Alvernia Hosp 1.341258 103.8366
37 51059 37 Thomson Rd AFT TOA PAYOH RISE 1.337033 103.8374
38 51049 38 Thomson Rd SLF Cplx 1.333821 103.8380
39 51039 39 Thomson Rd Opp S'pore Polo Club 1.331658 103.8389
40 51029 40 Thomson Rd Opp Old Police Acad 1.330255 103.8397
41 51019 41 Thomson Rd Thomson Flyover 1.328942 103.8407
42 50059 42 Thomson Rd Opp Thomson Med Ctr 1.325264 103.8422
43 50049 43 Thomson Rd Opp Novena Lodge 1.323541 103.8419
44 50037 44 Thomson Rd Bef Novena Stn Exit B 1.321100 103.8422
45 50069 45 Newton Rd Hotel Royal 1.317022 103.8416
46 40129 46 Newton Rd Newton Life Ch 1.314538 103.8408
47 40189 47 Scotts Rd Newton Stn Exit B 1.312274 103.8381
48 40179 48 Scotts Rd Env Bldg 1.311332 103.8369
49 9219 49 Scotts Rd Far East Plaza 1.307320 103.8332
50 9047 50 Orchard Rd Orchard Stn/Tang Plaza 1.304461 103.8329
51 9037 51 Orchard Rd Bef Cairnhill Rd 1.302340 103.8370
52 8138 52 Orchard Rd Concorde Hotel S'pore 1.300479 103.8418
53 8057 53 Orchard Rd Dhoby Ghaut Stn 1.299310 103.8453
54 8069 54 Bras Basah Rd Bencoolen Stn Exit B 1.298214 103.8494
55 4179 55 Bras Basah Rd Aft Bras Basah Stn Exit A 1.296479 103.8515
56 2049 56 Bras Basah Rd Raffles Hotel 1.294521 103.8540
57 2029 57 Esplanade Dr Aft Esplanade Stn Exit D 1.290106 103.8546
58 3019 58 Collyer Quay OUE Bayfront 1.284245 103.8531
59 3059 59 Raffles Quay One Raffles Quay 1.281111 103.8515
60 3129 60 Shenton Way UIC Bldg 1.278070 103.8496
61 3218 61 Shenton Way Opp MAS Bldg 1.274079 103.8469
62 5631 62 Cantonment Link Blk 16 1.273461 103.8403
63 5521 63 Cantonment Rd Maritime Hse 1.276769 103.8406
64 10021 64 Neil Rd Blk 3 1.277448 103.8384
65 10041 65 Kg Bahru Rd BEF KAMPONG BAHRU TER 1.276058 103.8352
66 10051 66 Jln Bt Merah Blk 149 1.277412 103.8321
67 10061 67 Jln Bt Merah Blk 140 1.278605 103.8295
68 10071 68 Jln Bt Merah Blk 111 1.280453 103.8264
69 10501 69 Jln Bt Merah Blk 104 1.281058 103.8253
70 10081 70 Jln Bt Merah Opp Blk 120 1.282278 103.8224
71 10009 71 Bt Merah Ctrl Bt Merah Int 1.282102 103.817269 Stops
BS_167_DIR_2_DF[order(BS_167_DIR_2_DF$Seq),] BS_Code Seq BS_Road_Name BS_Name Latitude Longitude
1 10009 1 Bt Merah Ctrl Bt Merah Int 1.282102 103.8172
2 10089 2 Jln Bt Merah Blk 119 1.282923 103.8217
3 10079 3 Jln Bt Merah Blk 201 1.280395 103.8271
4 10069 4 Jln Bt Merah Opp Blk 140 1.278555 103.8301
5 10059 5 Jln Bt Merah Opp Blk 149 1.277397 103.8328
6 10049 6 Kg Bahru Rd OPP KAMPONG BAHRU TER 1.276233 103.8348
7 10017 7 Eu Tong Sen St Aft Hosp Dr 1.278320 103.8376
8 5519 8 Cantonment Rd Blk 1G 1.275535 103.8411
9 5629 9 Cantonment Rd Opp Southpoint 1.273211 103.8419
10 3223 10 Anson Rd Tanjong Pagar Stn Exit C 1.275703 103.8463
11 3151 11 Cecil St Opp GB Bldg 1.278921 103.8478
12 3021 12 Cecil St Prudential Twr 1.282555 103.8500
13 3011 13 Fullerton Rd Fullerton Sq 1.285618 103.8534
14 2111 14 Esplanade Dr Esplanade Bridge 1.290956 103.8545
15 4111 15 Stamford Rd Capitol Bldg 1.293954 103.8514
16 4121 16 Stamford Rd SMU 1.296185 103.8496
17 8041 17 Orchard Rd YMCA 1.298110 103.8478
18 8031 18 Penang Rd Dhoby Ghaut Stn Exit B 1.298312 103.8453
19 8111 19 Penang Rd Winsland Hse 1.299869 103.8409
20 8121 20 Somerset Rd Somerset Stn 1.300276 103.8388
21 9011 21 Orchard Turn Opp Ngee Ann City 1.302113 103.8343
22 9023 22 Orchard Turn Opp Orchard Stn/ION 1.303237 103.8325
23 9212 23 Scotts Rd Royal Plaza On Scotts 1.307022 103.8327
24 40171 24 Scotts Rd Opp Env Bldg 1.310952 103.8358
25 40181 25 Scotts Rd Newton Stn Exit A 1.312641 103.8383
26 40121 26 Newton Rd Opp Newton Life Ch 1.314582 103.8405
27 50061 27 Newton Rd Opp Hotel Royal 1.317435 103.8414
28 50031 28 Thomson Rd Opp Novena Ch 1.321378 103.8417
29 50041 29 Thomson Rd Novena Lodge 1.323623 103.8416
30 50051 30 Thomson Rd Thomson Med Ctr 1.325878 103.8418
31 51011 31 Thomson Rd Opp Tan Tong Meng Twr 1.327842 103.8406
32 51021 32 Thomson Rd Old Police Acad 1.330411 103.8391
33 51031 33 Thomson Rd S'pore Polo Club 1.331629 103.8386
34 51051 34 Thomson Rd AFT ANDREW RD 1.336354 103.8371
35 51071 35 Thomson Rd MacRitchie Resvr 1.342193 103.8360
36 53011 36 Upp Thomson Rd ST. THERESA'S HME 1.345554 103.8383
37 53021 37 Upp Thomson Rd Lakeview Estate 1.349680 103.8365
38 53041 38 Upp Thomson Rd Bef Thomson Ridge 1.353099 103.8343
39 53051 39 Upp Thomson Rd Upp Thomson Stn Exit 5 1.354995 103.8316
40 53061 40 Upp Thomson Rd Bef Windsor Pk Rd 1.357158 103.8288
41 53071 41 Upp Thomson Rd Opp Flame Tree Pk 1.360633 103.8283
42 53081 42 Upp Thomson Rd Opp Faber Gdn 1.363193 103.8278
43 53091 43 Upp Thomson Rd Bef Ang Mo Kio Ave 1 1.364820 103.8279
44 56011 44 Upp Thomson Rd Bef adana at thomson 1.368650 103.8283
45 56021 45 Upp Thomson Rd Opp Sembawang Hills FC 1.372666 103.8284
46 56031 46 Upp Thomson Rd Bef Yio Chu Kang Rd 1.376957 103.8282
47 56041 47 Upp Thomson Rd Opp Meadows @ Peirce 1.379094 103.8276
48 56051 48 Upp Thomson Rd Opp Tagore Rd 1.382495 103.8258
49 56061 49 Upp Thomson Rd Aft Tagore Dr 1.385154 103.8229
50 56071 50 Upp Thomson Rd Bef Old Upp Thomson Rd 1.387177 103.8196
51 56081 51 Upp Thomson Rd Bef SLE 1.391569 103.8182
52 56091 52 Upp Thomson Rd Springleaf Stn Exit 3 1.396200 103.8185
53 57011 53 Upp Thomson Rd Opp Springleaf Nature Pk 1.400157 103.8172
54 57021 54 Sembawang Rd Forest Hills Condo 1.404141 103.8183
55 57031 55 Sembawang Rd Nee Soon Driclad Ctr 1.407054 103.8201
56 57041 56 Sembawang Rd Nee Soon HQ 22 SIB 1.410693 103.8219
57 57051 57 Sembawang Rd Aft Sembawang Air Base 1.415206 103.8234
58 57061 58 Sembawang Rd Dieppe Barracks 1.419905 103.8245
59 57071 59 Sembawang Rd Opp Khatib Camp 1.424958 103.8255
60 57081 60 Sembawang Rd Opp Blk 713 1.427536 103.8266
61 57111 61 Sembawang Rd Opp Blk 101 1.431236 103.8265
62 57121 62 Sembawang Rd Opp Blk 115B 1.433451 103.8263
63 57131 63 Sembawang Rd Opp Jln Kemuning 1.438084 103.8253
64 58011 64 Sembawang Rd Opp Sembawang Shop Ctr 1.441356 103.8242
65 58021 65 Sembawang Rd Opp The Nautical 1.443241 103.8237
66 58031 66 Sembawang Rd Opp Canberra Dr 1.446287 103.8250
67 58339 67 Canberra Link Opp Blk 589D 1.449361 103.8243
68 58159 68 Canberra Rd Aft Admiral Hill 1.448217 103.8224
69 58009 69 Sembawang Vista Sembawang Int 1.447482 103.8194sf_BS_167_DIR_1 <- st_as_sf(BS_167_DIR_1_DF, coords = c("Longitude", "Latitude"), crs = 4326)
sf_BS_167_DIR_2 <- st_as_sf(BS_167_DIR_2_DF, coords = c("Longitude", "Latitude"), crs = 4326)
sf_BS_167_DIR_1 <- st_transform(sf_BS_167_DIR_1, crs = 3414)
sf_BS_167_DIR_2 <- st_transform(sf_BS_167_DIR_2, crs = 3414)tmap_mode("view")
tm_shape(sf_BS_167_DIR_1) +
tm_dots(col = "red") +
tm_shape(sf_BS_167_DIR_2) +
tm_dots(col = "blue")gen_od_timing <- function(input_OD, sf_bs, timing){
OD_TEST_DIR1 <- input_OD %>% filter(DAY_TYPE == "WEEKDAY" & TIME_PER_HOUR == timing)
OD_TEST_DIR1 <- OD_TEST_DIR1[5:7]
sf <- od_to_sf(OD_TEST_DIR1, sf_bs)
return (sf)
}
tmap_plot_route <- function(BS, OD) {
tmap_mode("view")
tm_shape(BS) +
tm_dots(col = "magenta", scale = 1.3) +
#tm_shape(sf_BS_167_DIR_2) +
# tm_dots(col = "blue", scale = 2) +
tm_shape(OD) +
tm_lines(col = "TOTAL_TRIPS", style="fixed", breaks = c(0, 1, 5, 10, 15, 25, 50, 80, 130, 250), lwd = "TOTAL_TRIPS", scale=20, palette="viridis")
}
plot_trip_summary <- function(OD){
summary(OD$TOTAL_TRIPS)
p <- ggplot(OD, aes(x=TOTAL_TRIPS)) + geom_histogram(binwidth=25) + xlim(0, 500) + ylim(0, 50)
ggplotly(p)
}temp_sf <- gen_od_timing(OD_2023_11_DIR1, sf_BS_167_DIR_1, 6)
tmap_plot_route(sf_BS_167_DIR_1, temp_sf)temp_sf %>% arrange(desc(TOTAL_TRIPS))Simple feature collection with 856 features and 3 fields
Geometry type: LINESTRING
Dimension: XY
Bounding box: xmin: 26208.89 ymin: 28438.32 xmax: 30372.89 ymax: 47930.55
Projected CRS: SVY21 / Singapore TM
First 10 features:
ORIGIN_PT_CODE DESTINATION_PT_CODE TOTAL_TRIPS
1 40189 9219 39.000000
2 56099 56059 33.095238
3 57029 56099 20.428571
4 56029 53059 12.761905
5 8057 2049 10.809524
6 8057 8069 10.047619
7 56019 53059 9.285714
8 56059 53059 8.333333
9 40189 40179 7.666667
10 57079 56099 6.333333
geometry
1 LINESTRING (28532.13 32730....
2 LINESTRING (26387.61 41995....
3 LINESTRING (26364.54 42868....
4 LINESTRING (27486.01 39510....
5 LINESTRING (29332.34 31296....
6 LINESTRING (29332.34 31296....
7 LINESTRING (27476.66 39056....
8 LINESTRING (27190.6 40516.1...
9 LINESTRING (28532.13 32730....
10 LINESTRING (27148.6 45132.7...plot_trip_summary(temp_sf)temp_sf <- gen_od_timing(OD_2023_11_DIR1, sf_BS_167_DIR_1, 7)
tmap_plot_route(sf_BS_167_DIR_1, temp_sf)plot_trip_summary(temp_sf)temp_sf <- gen_od_timing(OD_2023_11_DIR1, sf_BS_167_DIR_1, 8)
tmap_plot_route(sf_BS_167_DIR_1, temp_sf)plot_trip_summary(temp_sf)temp_sf <- gen_od_timing(OD_2023_11_DIR1, sf_BS_167_DIR_1, 9)
tmap_plot_route(sf_BS_167_DIR_1, temp_sf)plot_trip_summary(temp_sf)Animation of 24hrs
i_time = 5
tm_objs = list()
while (i_time < 24){
temp_sf <- gen_od_timing(OD_2023_11_DIR1, sf_BS_167_DIR_1, i_time)
result = paste("Bus Service 167 Weekday: Hour ", i_time, sep = " ")
temp_tm <-
#tm_shape(sf_BS_167_DIR_2) +
# tm_dots(col = "blue", scale = 2) +
tm_shape(mpsz, bbox = c(22000, 27000, 34000, 49000)) +
tm_polygons(alpha=0) +
tm_shape(temp_sf) +
tm_lines("TOTAL_TRIPS", style="fixed", breaks = c(0, 25, 50, 100, 250, 500, 700, 1000, 1500, 2500), lwd = "TOTAL_TRIPS", scale=10, palette="-viridis", alpha=0.8) +
tm_shape(sf_BS_167_DIR_1) +
tm_dots(col = "magenta", scale = 3, labels="BS_Code", ) +
tm_text("BS_Code", col="black", size=0.8)+
tm_layout(legend.position = c("right", "top"),
title = result,
title.position = c('right', 'top')
)
tm_objs <- append(tm_objs, list(temp_tm))
i_time = i_time + 1
}
tmap_animation(tm_objs,filename = "data/167_OD_analysis/test.gif", width=2500, height=1500, dpi=200, outer.margins = 0)I guess not very clear so we will analyse at subzone level
5 Data Analysis - Intra-Zonal Flows
Analyse by combining trips into subzone level to have a rough overview
mpsz_pln_area <- st_read(dsn = "data/167_OD_analysis/",
layer = "MP14_PLNG_AREA_WEB_PL") %>%
st_transform(crs = 3414)Reading layer `MP14_PLNG_AREA_WEB_PL' from data source
`C:\Users\boylu\OneDrive - Singapore Management University\0_git-projects\urbancoalesce\explore\data\167_OD_analysis'
using driver `ESRI Shapefile'
Simple feature collection with 55 features and 12 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21sf_BS_167_DIR_1_MPSZ <- st_intersection(sf_BS_167_DIR_1, mpsz_pln_area) %>%
select(BS_Code, PLN_AREA_C)
BUS_STOP_DF_MPSZ <- sf_BS_167_DIR_1_MPSZ %>% st_drop_geometry()5.1 Normalisation of values
OD Matrix shows flows between pairs of bus stops. For bus stop pairs served by different services, they will contribute to the same OD count.
This is especially evident in certain bus stop pairs, such as BS 40189 Newton Stn Exit B to BS 09219 Far East Plaza, amongst others, where commuters would likely hop on any of the many buses along the road.
5.1.0.1 Methodology
We will use this formula in calculating and estimating the OD flows for 167 bus stops using this formula below.
<insert latex formula>
Bus Per Hour (BPH) for given service at given hour = 60 / Average Frequency at given timing for the given service*
* if given service operates at 8 - 12 min during AM Peak, 10 minutes will be used, hence, computing a 10 min interval between buses and 6 buses per hour (bph)
Flow between OD Pair for a given service at given hour = Total Flow between OD Pair at given hr x (BPH for given service at given hour / Sum of BPH on all services servicing between OD Pair at given hr) ^
5.1.0.2 ^ Rules
Trip distance of services shall not exceed 3km of mean distance of all services between the OD pair for normal services (non-express)
- If services takes a large detour, we assume that passengers will not be inclined to take the detour of services since its longer
Exclusion of services that charges express fares (Express or City Direct) if OD pair is less than 5km (ie. shorter than ‘express’ sector)
Issues with assumptions:
Assumes uniform distribution across all services
For express services with express sectors, it is hard to estimate the split of passengers taking normal and express services - Assumption will be that they are equal
5.1.0.3 Computing
As LTA’s data does not provide a differentiation for bus frequencies on weekdays and weekends, we will take the average time between the intervals provided
Additionally, we will derive the frequencies as follows:
For the following timings below, LTA’s definition will be used, we will use the average of the frequency band provided:
AM Peak - 0630h to 0830h
AM Off Peak - 0831h to 1659h
PM Peak - 1700h to 1900h
PM Off Peak - after 1900h
For the following timings below:
- From start of service to 0630h - We take upper limit of AM Peak frequency
norm_bph <- function(BUS_SVC, DIR, HOUR, NUM_DAY_OF_WEEK, BS_CODE_ORIGIN, BS_SEQ_ORIGIN){
# check operational
if (HOUR < 3){
HOUR <- HOUR + 24
if (NUM_DAY_OF_WEEK > 1){
NUM_DAY_OF_WEEK <- NUM_DAY_OF_WEEK - 1
}
else{
NUM_DAY_OF_WEEK <- 7
}
}
total_time <- norm_bph_bs_operational(BUS_SVC, DIR, HOUR, NUM_DAY_OF_WEEK, BS_CODE_ORIGIN, BS_SEQ_ORIGIN)
if (total_time == -1){
return (0)
}
else {
SEL_BUS_SVC <- BUS_SVC_DF %>% filter(Service_No == BUS_SVC & Direction == DIR)
if (HOUR >= 19){
time <- strsplit(SEL_BUS_SVC$PM_OffPeak_Freq, "-")
freq <- (as.numeric(time[[1]][1]) + as.numeric(time[[1]][2])) / 2
bph <- total_time / freq
}
else if(HOUR >= 17 && HOUR < 19){
time <- strsplit(SEL_BUS_SVC$PM_Peak_Freq, "-")
freq <- (as.numeric(time[[1]][1]) + as.numeric(time[[1]][2])) / 2
bph <- total_time / freq
}
else if(HOUR >= 9 && HOUR < 17){
time <- strsplit(SEL_BUS_SVC$AM_OffPeak_Freq, "-")
freq <- (as.numeric(time[[1]][1]) + as.numeric(time[[1]][2])) / 2
bph <- total_time / freq
}
else if(HOUR == 8){
time <- strsplit(SEL_BUS_SVC$AM_Peak_Freq, "-")
freq1 <- (as.numeric(time[[1]][1]) + as.numeric(time[[1]][2])) / 2
time <- strsplit(SEL_BUS_SVC$AM_OffPeak_Freq, "-")
freq2 <- (as.numeric(time[[1]][1]) + as.numeric(time[[1]][2])) / 2
if (total_time <= 30){
bph <- total_time / freq1
}
else{
temp_bph <- total_time / freq1
bph <- ((((total_time - 30) / freq2) + temp_bph) / 2)
}
}
else if(HOUR == 7){
time <- strsplit(SEL_BUS_SVC$AM_Peak_Freq, "-")
freq <- (as.numeric(time[[1]][1]) + as.numeric(time[[1]][2])) / 2
bph <- total_time / freq
}
else if(HOUR == 6){
time <- strsplit(SEL_BUS_SVC$AM_Peak_Freq, "-")
freq1 <- as.numeric(time[[1]][2])
time <- strsplit(SEL_BUS_SVC$AM_Peak_Freq, "-")
freq2 <- (as.numeric(time[[1]][1]) + as.numeric(time[[1]][2])) / 2
if (total_time <= 30){
bph <- total_time / freq1
}
else{
temp_bph <- total_time / freq1
bph <- ((((total_time - 30) / freq2) + temp_bph) / 2)
}
}
else if(HOUR >= 4 && HOUR < 6){
time <- strsplit(SEL_BUS_SVC$AM_Peak_Freq, "-")
freq <- as.numeric(time[[1]][2])
bph <- total_time / freq
}
return (bph)
}
}
norm_bph_bs_operational <- function(BUS_SVC, DIR, HOUR, NUM_DAY_OF_WEEK, BS_CODE_ORIGIN, BS_SEQ_ORIGIN){
# BS_SEQ_ORIGIN verifies that it is the correct bus stop (eg. starting stop instead of ending terminus)
SEL_BUS_ROUTE <- BUS_ROUTE_DF %>% filter(Service_No == BUS_SVC & BS_Code == BS_CODE_ORIGIN & Direction == DIR & Seq == BS_SEQ_ORIGIN)
temp_timing <- -1
if (NUM_DAY_OF_WEEK > 0 & NUM_DAY_OF_WEEK < 6){
if (is.na(SEL_BUS_ROUTE$WD_FirstBus)){
return (-1)
}
if (SEL_BUS_ROUTE$WD_LastBus < 3 * 100){
WD_LastBus <- 2400 + SEL_BUS_ROUTE$WD_LastBus
}
else{
WD_LastBus <- SEL_BUS_ROUTE$WD_LastBus
}
if ((SEL_BUS_ROUTE$WD_FirstBus < (as.numeric(HOUR) * 100)) && (WD_LastBus > as.numeric(HOUR) * 100)){
temp_timing <- WD_LastBus - as.numeric(HOUR) * 100
}
else if ((SEL_BUS_ROUTE$WD_FirstBus == (as.numeric(HOUR) * 100)) && (WD_LastBus > as.numeric(HOUR) * 100)){
temp_timing <- as.numeric(HOUR) * 100 + 60 - SEL_BUS_ROUTE$WD_FirstBus
}
else if ((SEL_BUS_ROUTE$WD_FirstBus == (as.numeric(HOUR) * 100)) && (WD_LastBus == as.numeric(HOUR) * 100)){
temp_timing <- WD_LastBus - SEL_BUS_ROUTE$WD_FirstBus
}
}
else if (NUM_DAY_OF_WEEK == 6){
if (is.na(SEL_BUS_ROUTE$SAT_FirstBus)){
return (-1)
}
if (SEL_BUS_ROUTE$SAT_LastBus < 3 * 100){
Sat_LastBus <- 2400 + SEL_BUS_ROUTE$SAT_LastBus
}
else{
Sat_LastBus <- SEL_BUS_ROUTE$SAT_LastBus
}
if ((SEL_BUS_ROUTE$SAT_FirstBus < (as.numeric(HOUR) * 100)) && (Sat_LastBus > as.numeric(HOUR) * 100)){
temp_timing <- Sat_LastBus - as.numeric(HOUR) * 100
}
else if ((SEL_BUS_ROUTE$SAT_FirstBus == (as.numeric(HOUR) * 100)) && (Sat_LastBus > as.numeric(HOUR) * 100)){
temp_timing <- as.numeric(HOUR) * 100 + 60 - SEL_BUS_ROUTE$SAT_FirstBus
}
else if ((SEL_BUS_ROUTE$SAT_FirstBus == (as.numeric(HOUR) * 100)) && (Sat_LastBus == as.numeric(HOUR) * 100)){
temp_timing <- Sat_LastBus - SEL_BUS_ROUTE$SAT_FirstBus
}
}
else if (NUM_DAY_OF_WEEK == 7){
if (is.na(SEL_BUS_ROUTE$SUN_FirstBus)){
return (-1)
}
if (SEL_BUS_ROUTE$SUN_LastBus < 3 * 100){
Sun_LastBus <- 2400 + SEL_BUS_ROUTE$SUN_LastBus
}
else{
Sun_LastBus <- SEL_BUS_ROUTE$SUN_LastBus
}
if ((SEL_BUS_ROUTE$SUN_FirstBus < (as.numeric(HOUR) * 100)) && (Sun_LastBus > as.numeric(HOUR) * 100)){
temp_timing <- Sun_LastBus - as.numeric(HOUR) * 100
}
else if ((SEL_BUS_ROUTE$SUN_FirstBus == (as.numeric(HOUR) * 100)) && (Sun_LastBus > as.numeric(HOUR) * 100)){
temp_timing <- as.numeric(HOUR) * 100 + 60 - SEL_BUS_ROUTE$SUN_FirstBus
}
else if ((SEL_BUS_ROUTE$SUN_FirstBus == (as.numeric(HOUR) * 100)) && (Sun_LastBus == as.numeric(HOUR) * 100)){
temp_timing <- Sun_LastBus - SEL_BUS_ROUTE$SUN_FirstBus
}
}
if (temp_timing > 60){
return (60)
}
else{
return (temp_timing)
}
}
norm_common_bus_svcs <- function(BS_CODE_ORIGIN, BS_CODE_DEST){
TEMP_BUS_ROUTE_DF <- BUS_ROUTE_DF[1:5]
# Filter ensures correct pairs are selected (in event of start/end stop same) and filters out inaccurate data where stops are recorded in reverse (eg. 167 Nov 8am Weekday data between 3218 and 3129)
TEMP_BUS_ROUTE_DF <- left_join(TEMP_BUS_ROUTE_DF, TEMP_BUS_ROUTE_DF, by=c("Service_No", "Direction"), suffix=c("Origin", "Dest")) %>% filter(SeqOrigin < SeqDest)
TEMP_BUS_ROUTE_OD_PAIR <- TEMP_BUS_ROUTE_DF %>% filter(BS_CodeOrigin == BS_CODE_ORIGIN & BS_CodeDest == BS_CODE_DEST)
}
norm_verify_express <- function(BUS_SVC, DIR){
SEL_BUS_SVC <- BUS_SVC_DF %>% filter(Service_No == BUS_SVC & DIR == Direction)
BUS_CAT <- c("CITY_LINK", "EXPRESS")
if (SEL_BUS_SVC$Category %in% BUS_CAT){
return (FALSE)
}
return (TRUE)
}
norm_calc_bph <- function(BS_CODE_ORIGIN, BS_CODE_DEST, HOUR, NUM_DAY_OF_WEEK, SVC){
common_svcs <- norm_common_bus_svcs(BS_CODE_ORIGIN, BS_CODE_DEST)
common_svcs <- common_svcs %>% mutate(mileage = DistanceDest - DistanceOrigin)
mean_mileage <- mean(common_svcs$mileage)
bph_total <- 0
bph_svc <- 0
# if no rows (ie. all invalid data, dont run bph)
if (nrow(common_svcs) > 0){
for (i in 1:nrow(common_svcs)) {
if (common_svcs[i,]$mileage <= mean_mileage + 3){
temp_bph <- norm_bph(common_svcs[i,]$Service_No, common_svcs[i,]$Direction, HOUR, NUM_DAY_OF_WEEK, BS_CODE_ORIGIN, common_svcs[i,]$SeqOrigin)
if (common_svcs[i,]$Service_No == SVC){
bph_svc <- temp_bph
bph_total <- bph_total + temp_bph
}
# Let us verify for express service
else if (common_svcs[i,]$mileage <= 5){
if (norm_verify_express(common_svcs[i,]$Service_No, common_svcs[i,]$Direction)){
bph_total <- bph_total + temp_bph
}
}
else{
bph_total <- bph_total + temp_bph
}
}
}
}
bph <- list(bph_svc, bph_total)
return (bph)
}
norm_flow_od <- function(FLOW, BS_CODE_ORIGIN, BS_CODE_DEST, HOUR, NUM_DAY_OF_WEEK, SVC){
bph <- norm_calc_bph(BS_CODE_ORIGIN, BS_CODE_DEST, HOUR, NUM_DAY_OF_WEEK, SVC)
bph_svc <- bph[[1]]
bph_total <- bph[[2]]
svc_flow <- FLOW * (bph_svc / bph_total)
if (is.nan(svc_flow)){
return (0)
}
return (svc_flow)
}5.2 Generating Flows
gen_od_timing_flows <- function(input_OD, sf_bs, timing){
OD_TEST_DIR1 <- input_OD %>% filter(DAY_TYPE == "WEEKDAY" & TIME_PER_HOUR == timing)
#norm_flow_od <- function(FLOW, BS_CODE_ORIGIN, BS_CODE_DEST, HOUR, NUM_DAY_OF_WEEK, SVC){
OD_TEST_DIR1 <- OD_TEST_DIR1 %>% rowwise() %>% mutate(NORM_TRIPS = (norm_flow_od(TOTAL_TRIPS, ORIGIN_PT_CODE, DESTINATION_PT_CODE, TIME_PER_HOUR, 1, "167")))
OD_2023_11_DIR1 <- left_join(OD_TEST_DIR1 , sf_bs, by = c("ORIGIN_PT_CODE" = "BS_Code")) %>% rename(ORIGIN_BS = ORIGIN_PT_CODE, ORIGIN_PA = PLN_AREA_C, DESTIN_BS = DESTINATION_PT_CODE)
OD_2023_11_DIR1 <- left_join(OD_2023_11_DIR1 , sf_bs, by = c("DESTIN_BS" = "BS_Code"))
return (OD_2023_11_DIR1)
}
gen_od_timing_PA <- function(OD_2023_11_DIR1_PA){
OD_2023_11_DIR1_PA <- OD_2023_11_DIR1_PA %>% rename(DESTIN_PA = PLN_AREA_C) %>% drop_na() %>% group_by(ORIGIN_PA, DESTIN_PA) %>% summarise(PA_TRIPS = sum(NORM_TRIPS))
return (OD_2023_11_DIR1_PA)
}
gen_od_timing_PA_intra <- function(OD_2023_11_DIR1_PA){
OD_2023_11_DIR1_PA_INTRA <- OD_2023_11_DIR1_PA[OD_2023_11_DIR1_PA$ORIGIN_PA!=OD_2023_11_DIR1_PA$DESTIN_PA,]
return (OD_2023_11_DIR1_PA_INTRA)
}
gen_od_timing_PA_inter <- function(OD_2023_11_DIR1_PA){
OD_2023_11_DIR1_PA_INTER <- OD_2023_11_DIR1_PA[OD_2023_11_DIR1_PA$ORIGIN_PA==OD_2023_11_DIR1_PA$DESTIN_PA,]
return (OD_2023_11_DIR1_PA_INTER)
}
gen_od_timing_PA_flows <- function(OD_2023_11_DIR1_PA_INTRA){
sf_OD_2023_11_DIR1_PA_INTRA_FLOWS <- od2line(flow = OD_2023_11_DIR1_PA_INTRA, zones = mpsz_pln_area, zone_code = "PLN_AREA_C")
return (sf_OD_2023_11_DIR1_PA_INTRA_FLOWS)
}
tmap_plot_pa <- function(BS, OD) {
tmap_mode("view")
tm_shape(mpsz_pln_area) +
tm_polygons("PLN_AREA_C", legend.show = FALSE, palette="Set3") +
tm_shape(BS) +
tm_dots("PLN_AREA_C", scale = 1.3, legend.show = FALSE, palette="Set3") +
#tm_shape(sf_BS_167_DIR_2) +
# tm_dots(col = "blue", scale = 2) +
tm_shape(OD) +
tm_lines(col = "PA_TRIPS", style="fixed", breaks = c(0, 1, 5, 10, 15, 25, 50, 80, 130, 250), lwd = "PA_TRIPS", scale=20, palette="viridis")
}
plot_trip_summary <- function(OD){
summary(OD$NORM_TRIPS)
p <- ggplot(OD, aes(x=NORM_TRIPS)) + geom_histogram(binwidth=25) + xlim(0, 500) + ylim(0, 50)
ggplotly(p)
}Pre-generate results
temp_sf_pa <- gen_od_timing_flows(OD_2023_11_DIR1, BUS_STOP_DF_MPSZ, 6)
saveRDS(temp_sf_pa, "data/167_OD_Analysis/sf_norm_6.rds")
temp_sf_pa <- gen_od_timing_flows(OD_2023_11_DIR1, BUS_STOP_DF_MPSZ, 7)
saveRDS(temp_sf_pa, "data/167_OD_Analysis/sf_norm_7.rds")
temp_sf_pa <- gen_od_timing_flows(OD_2023_11_DIR1, BUS_STOP_DF_MPSZ, 8)
saveRDS(temp_sf_pa, "data/167_OD_Analysis/sf_norm_8.rds")
temp_sf_pa <- gen_od_timing_flows(OD_2023_11_DIR1, BUS_STOP_DF_MPSZ, 9)
saveRDS(temp_sf_pa, "data/167_OD_Analysis/sf_norm_9.rds")
temp_sf_pa <- gen_od_timing_flows(OD_2023_11_DIR1, BUS_STOP_DF_MPSZ, 10)
saveRDS(temp_sf_pa, "data/167_OD_Analysis/sf_norm_10.rds")
temp_sf_pa <- gen_od_timing_flows(OD_2023_11_DIR1, BUS_STOP_DF_MPSZ, 11)
saveRDS(temp_sf_pa, "data/167_OD_Analysis/sf_norm_11.rds")
temp_sf_pa <- gen_od_timing_flows(OD_2023_11_DIR1, BUS_STOP_DF_MPSZ, 12)
saveRDS(temp_sf_pa, "data/167_OD_Analysis/sf_norm_12.rds")
temp_sf_pa <- gen_od_timing_flows(OD_2023_11_DIR1, BUS_STOP_DF_MPSZ, 18)
saveRDS(temp_sf_pa, "data/167_OD_Analysis/sf_norm_18.rds")
temp_sf_pa <- gen_od_timing_flows(OD_2023_11_DIR1, BUS_STOP_DF_MPSZ, 19)
saveRDS(temp_sf_pa, "data/167_OD_Analysis/sf_norm_19.rds")
temp_sf_pa <- gen_od_timing_flows(OD_2023_11_DIR1, BUS_STOP_DF_MPSZ, 20)
saveRDS(temp_sf_pa, "data/167_OD_Analysis/sf_norm_20.rds")tmap_options(check.and.fix = TRUE)
temp_sf <- readRDS("data/167_OD_Analysis/sf_norm_6.rds")
temp_sf_pa <- gen_od_timing_PA(temp_sf)
temp_sf_pa_intra <- gen_od_timing_PA_intra(temp_sf_pa)
temp_sf_pa_inter <- gen_od_timing_PA_inter(temp_sf_pa)
temp_sf_pa_intra_flows <- gen_od_timing_PA_flows(temp_sf_pa_intra)
tmap_plot_pa(sf_BS_167_DIR_1_MPSZ, temp_sf_pa_intra_flows)st_drop_geometry(temp_sf_pa_intra_flows) %>% arrange(desc(PA_TRIPS)) ORIGIN_PA DESTIN_PA PA_TRIPS
1 YS AM 54.74234771
2 AM BS 38.51508192
3 SB YS 25.77903551
4 YS BS 15.42857143
5 YS NV 9.19047619
6 YS TP 9.14285714
7 SB BS 6.19532364
8 SB TP 4.83033932
9 SB AM 4.21642429
10 YS OR 3.85714286
11 SB OR 3.61904762
12 AM TP 3.42857143
13 YS DT 3.28571429
14 AM OR 2.38095238
15 AM NV 2.28571429
16 AM DT 2.23809524
17 SB BM 1.76190476
18 AM MU 1.28571429
19 YS NT 1.28571429
20 SB NV 1.26603935
21 SB NT 0.85714286
22 BS TP 0.83564537
23 YS BM 0.80952381
24 YS MU 0.66666667
25 BS OR 0.55535831
26 BS DT 0.50757576
27 SB MU 0.33333333
28 SB DT 0.28571429
29 AM NT 0.19047619
30 BS NV 0.18881430
31 AM BM 0.04761905
32 BS MU 0.01839827
33 BM OR 0.00000000
34 BS BM 0.00000000
35 BS NT 0.00000000
36 DT BM 0.00000000
37 MU BM 0.00000000
38 MU DT 0.00000000
39 NT BM 0.00000000
40 NT DT 0.00000000
41 NT MU 0.00000000
42 NT OR 0.00000000
43 NV BM 0.00000000
44 NV DT 0.00000000
45 NV MU 0.00000000
46 NV NT 0.00000000
47 NV OR 0.00000000
48 OR BM 0.00000000
49 OR DT 0.00000000
50 OR MU 0.00000000
51 TP BM 0.00000000
52 TP DT 0.00000000
53 TP MU 0.00000000
54 TP NT 0.00000000
55 TP NV 0.00000000
56 TP OR 0.00000000plot_trip_summary(temp_sf)temp_sf_pa_inter %>% arrange(desc(PA_TRIPS))# A tibble: 11 × 3
# Groups: ORIGIN_PA [11]
ORIGIN_PA DESTIN_PA PA_TRIPS
<chr> <chr> <dbl>
1 YS YS 71.5
2 SB SB 3.57
3 BS BS 1.68
4 AM AM 1.21
5 BM BM 0
6 DT DT 0
7 MU MU 0
8 NT NT 0
9 NV NV 0
10 OR OR 0
11 TP TP 0 tmap_options(check.and.fix = TRUE)
temp_sf <- readRDS("data/167_OD_Analysis/sf_norm_7.rds")
temp_sf_pa <- gen_od_timing_PA(temp_sf)
temp_sf_pa_intra <- gen_od_timing_PA_intra(temp_sf_pa)
temp_sf_pa_inter <- gen_od_timing_PA_inter(temp_sf_pa)
temp_sf_pa_intra_flows <- gen_od_timing_PA_flows(temp_sf_pa_intra)
tmap_plot_pa(sf_BS_167_DIR_1_MPSZ, temp_sf_pa_intra_flows)st_drop_geometry(temp_sf_pa_intra_flows) %>% arrange(desc(PA_TRIPS)) ORIGIN_PA DESTIN_PA PA_TRIPS
1 YS AM 33.57826831
2 SB YS 31.09948302
3 AM BS 28.73420840
4 YS BS 11.18796992
5 OR DT 10.57549732
6 SB AM 9.85964912
7 NV DT 9.61231495
8 NV NT 9.10384571
9 MU DT 9.05398997
10 BS NV 8.55373148
11 NT OR 7.77622980
12 SB BS 7.39097744
13 BS TP 5.89719357
14 NV OR 5.80429713
15 YS OR 5.47619048
16 DT BM 5.15935139
17 AM TP 5.15288221
18 TP NV 4.82434563
19 YS NV 4.66917293
20 AM OR 4.57142857
21 AM NV 3.88471178
22 NT BM 3.73729941
23 NV MU 3.31962520
24 YS DT 2.71428571
25 BS OR 2.38187633
26 YS TP 2.26566416
27 BS DT 2.07088989
28 AM MU 2.00000000
29 TP OR 1.96015832
30 NV BM 1.76207235
31 OR BM 1.66452805
32 SB DT 1.52380952
33 TP DT 1.47252747
34 AM DT 1.38095238
35 YS NT 1.33333333
36 SB TP 1.22055138
37 NT DT 1.12380952
38 TP NT 1.11875822
39 OR MU 0.99298293
40 MU BM 0.88216961
41 AM NT 0.85714286
42 BS BM 0.82509158
43 BS NT 0.80000000
44 BS MU 0.78095238
45 YS MU 0.76190476
46 SB OR 0.61904762
47 TP MU 0.59047619
48 SB MU 0.57142857
49 SB NV 0.48120301
50 TP BM 0.44041154
51 SB BM 0.42857143
52 NT MU 0.37895761
53 YS BM 0.23809524
54 AM BM 0.19047619
55 BM SB 0.04761905
56 SB NT 0.04761905
57 BM MU 0.00000000
58 BM OR 0.00000000plot_trip_summary(temp_sf)temp_sf_pa_inter %>% arrange(desc(PA_TRIPS))# A tibble: 11 × 3
# Groups: ORIGIN_PA [11]
ORIGIN_PA DESTIN_PA PA_TRIPS
<chr> <chr> <dbl>
1 YS YS 40.9
2 BM BM 20.7
3 SB SB 8.32
4 BS BS 5.22
5 DT DT 2.40
6 MU MU 2.01
7 TP TP 1.66
8 NV NV 1.65
9 OR OR 1.29
10 AM AM 1.24
11 NT NT 0.279tmap_options(check.and.fix = TRUE)
temp_sf <- readRDS("data/167_OD_Analysis/sf_norm_8.rds")
temp_sf_pa <- gen_od_timing_PA(temp_sf)
temp_sf_pa_intra <- gen_od_timing_PA_intra(temp_sf_pa)
temp_sf_pa_inter <- gen_od_timing_PA_inter(temp_sf_pa)
temp_sf_pa_intra_flows <- gen_od_timing_PA_flows(temp_sf_pa_intra)
tmap_plot_pa(sf_BS_167_DIR_1_MPSZ, temp_sf_pa_intra_flows)st_drop_geometry(temp_sf_pa_intra_flows) %>% arrange(desc(PA_TRIPS)) ORIGIN_PA DESTIN_PA PA_TRIPS
1 YS AM 54.2710323
2 AM BS 31.6403363
3 MU DT 30.4413848
4 SB YS 28.7579756
5 NV OR 18.0658537
6 OR DT 17.9298779
7 NV NT 17.8644522
8 NV DT 14.8688962
9 NT OR 13.1950568
10 YS BS 11.1175987
11 BS NV 9.1853777
12 DT BM 8.2857811
13 SB AM 8.0134077
14 BS OR 6.8846852
15 TP NV 6.3871104
16 BS TP 6.0623215
17 NV MU 5.7794747
18 SB BS 5.2853683
19 NT BM 5.1880693
20 OR BM 4.8051573
21 YS OR 3.9523810
22 NV BM 3.7346189
23 YS NV 3.6384672
24 AM NV 2.8946412
25 AM DT 2.8571429
26 BS DT 2.7208639
27 TP OR 2.5683165
28 YS TP 2.5591454
29 MU BM 2.2396705
30 SB OR 2.0000000
31 OR MU 1.9864865
32 AM OR 1.8571429
33 NT DT 1.7244444
34 TP NT 1.7220120
35 SB NV 1.6173608
36 TP DT 1.5734196
37 YS NT 1.5714286
38 BS BM 1.4939711
39 BS MU 1.2793651
40 AM MU 1.2380952
41 SB DT 1.1428571
42 YS DT 1.1428571
43 NT MU 0.9330574
44 SB BM 0.9047619
45 SB MU 0.8571429
46 YS MU 0.8571429
47 BS NT 0.7932275
48 TP BM 0.7242923
49 SB NT 0.7142857
50 AM TP 0.6502746
51 TP MU 0.6158730
52 AM BM 0.5238095
53 AM NT 0.5238095
54 YS BM 0.5238095
55 SB TP 0.3106700
56 BM SB 0.1904762
57 BM MU 0.0000000
58 BM OR 0.0000000plot_trip_summary(temp_sf)temp_sf_pa_inter %>% arrange(desc(PA_TRIPS))# A tibble: 10 × 3
# Groups: ORIGIN_PA [10]
ORIGIN_PA DESTIN_PA PA_TRIPS
<chr> <chr> <dbl>
1 YS YS 42.2
2 BM BM 23.3
3 SB SB 15.5
4 BS BS 8.02
5 MU MU 6.15
6 DT DT 4.56
7 NV NV 4.37
8 TP TP 1.72
9 AM AM 0.927
10 NT NT 0.388tmap_options(check.and.fix = TRUE)
temp_sf <- readRDS("data/167_OD_Analysis/sf_norm_9.rds")
temp_sf_pa <- gen_od_timing_PA(temp_sf)
temp_sf_pa_intra <- gen_od_timing_PA_intra(temp_sf_pa)
temp_sf_pa_inter <- gen_od_timing_PA_inter(temp_sf_pa)
temp_sf_pa_intra_flows <- gen_od_timing_PA_flows(temp_sf_pa_intra)
tmap_plot_pa(sf_BS_167_DIR_1_MPSZ, temp_sf_pa_intra_flows)st_drop_geometry(temp_sf_pa_intra_flows) %>% arrange(desc(PA_TRIPS)) ORIGIN_PA DESTIN_PA PA_TRIPS
1 SB YS 29.6985009
2 AM BS 23.2781101
3 MU DT 20.8638621
4 NT OR 17.4732003
5 NV OR 16.7090113
6 YS AM 16.5693830
7 OR DT 14.6183124
8 NV NT 11.0546522
9 YS BS 10.2073733
10 NV DT 8.4729652
11 DT BM 6.8964398
12 BS OR 6.1384924
13 BS NV 6.1138363
14 NV MU 5.2645030
15 SB BS 4.7250384
16 SB AM 4.5161290
17 AM OR 4.2380952
18 OR BM 3.9632109
19 NT BM 3.8766230
20 BS TP 3.8475366
21 OR MU 3.8422232
22 TP NV 3.7558750
23 TP OR 3.3536203
24 YS OR 2.9047619
25 NT DT 2.5868132
26 YS NV 2.5145929
27 SB OR 2.3333333
28 NV BM 2.1551827
29 MU BM 2.1413903
30 AM NV 1.8156682
31 YS DT 1.5714286
32 YS TP 1.4976959
33 BS DT 1.3751257
34 TP DT 1.3334577
35 BS MU 1.2505495
36 SB BM 1.1428571
37 SB MU 1.0952381
38 BS NT 1.0373626
39 AM TP 1.0368664
40 NT MU 1.0043301
41 AM MU 0.9047619
42 SB TP 0.8663594
43 TP NT 0.8003565
44 SB NV 0.7987711
45 YS BM 0.6190476
46 YS MU 0.5714286
47 YS NT 0.5238095
48 BS BM 0.4877718
49 SB NT 0.4761905
50 SB DT 0.4285714
51 TP BM 0.3986178
52 TP MU 0.3384615
53 AM DT 0.3333333
54 AM BM 0.2857143
55 AM NT 0.1904762
56 BM SB 0.1904762
57 BM DT 0.0000000
58 BM MU 0.0000000
59 BM OR 0.0000000plot_trip_summary(temp_sf)temp_sf_pa_inter %>% arrange(desc(PA_TRIPS))# A tibble: 11 × 3
# Groups: ORIGIN_PA [11]
ORIGIN_PA DESTIN_PA PA_TRIPS
<chr> <chr> <dbl>
1 YS YS 24.6
2 BM BM 22.2
3 SB SB 10.0
4 BS BS 6.82
5 DT DT 5.52
6 MU MU 4.68
7 OR OR 3.73
8 NV NV 2.62
9 AM AM 0.972
10 TP TP 0.940
11 NT NT 0.177tmap_options(check.and.fix = TRUE)
temp_sf <- readRDS("data/167_OD_Analysis/sf_norm_10.rds")
temp_sf_pa <- gen_od_timing_PA(temp_sf)
temp_sf_pa_intra <- gen_od_timing_PA_intra(temp_sf_pa)
temp_sf_pa_inter <- gen_od_timing_PA_inter(temp_sf_pa)
temp_sf_pa_intra_flows <- gen_od_timing_PA_flows(temp_sf_pa_intra)
tmap_plot_pa(sf_BS_167_DIR_1_MPSZ, temp_sf_pa_intra_flows)st_drop_geometry(temp_sf_pa_intra_flows) %>% arrange(desc(PA_TRIPS)) ORIGIN_PA DESTIN_PA PA_TRIPS
1 SB YS 20.2682115
2 NT OR 18.9209250
3 AM BS 16.9221564
4 NV OR 16.4087523
5 OR DT 10.4447649
6 YS BS 9.4700461
7 MU DT 9.2002212
8 BS OR 8.2630268
9 YS AM 7.7442742
10 DT BM 6.6517715
11 NV NT 5.1921341
12 BS NV 5.1404578
13 NV DT 5.0336147
14 TP OR 5.0263685
15 YS OR 3.8095238
16 NV MU 3.8009183
17 OR MU 3.6188937
18 OR BM 2.9955352
19 AM OR 2.8571429
20 YS NV 2.8371736
21 SB BS 2.8033794
22 TP NV 2.7694628
23 BS TP 2.5509805
24 AM NV 1.9508449
25 BS MU 1.9289377
26 SB NV 1.8986175
27 SB OR 1.7142857
28 AM MU 1.5238095
29 NV BM 1.4459670
30 MU BM 1.4406464
31 SB AM 1.4270353
32 YS TP 1.3133641
33 NT BM 1.2401678
34 BS DT 1.2117380
35 NT DT 1.1589744
36 SB BM 1.1428571
37 NT MU 1.0716663
38 AM DT 0.9523810
39 AM TP 0.9216590
40 SB DT 0.9047619
41 YS MU 0.9047619
42 TP NT 0.8879778
43 TP MU 0.8461538
44 SB MU 0.8095238
45 BS NT 0.7010989
46 YS BM 0.6666667
47 YS DT 0.6666667
48 TP DT 0.5337539
49 BS BM 0.4167211
50 BM SB 0.3333333
51 SB TP 0.3010753
52 AM NT 0.2857143
53 YS NT 0.2857143
54 TP BM 0.2237055
55 SB NT 0.1904762
56 AM BM 0.0952381
57 BM MU 0.0000000
58 BM OR 0.0000000
59 MU OR 0.0000000plot_trip_summary(temp_sf)temp_sf_pa_inter %>% arrange(desc(PA_TRIPS))# A tibble: 11 × 3
# Groups: ORIGIN_PA [11]
ORIGIN_PA DESTIN_PA PA_TRIPS
<chr> <chr> <dbl>
1 YS YS 22.7
2 BM BM 18.9
3 SB SB 6.42
4 BS BS 4.84
5 OR OR 2.61
6 NV NV 2.48
7 DT DT 2.48
8 MU MU 2.29
9 AM AM 0.721
10 TP TP 0.372
11 NT NT 0.0744tmap_options(check.and.fix = TRUE)
temp_sf <- readRDS("data/167_OD_Analysis/sf_norm_11.rds")
temp_sf_pa <- gen_od_timing_PA(temp_sf)
temp_sf_pa_intra <- gen_od_timing_PA_intra(temp_sf_pa)
temp_sf_pa_inter <- gen_od_timing_PA_inter(temp_sf_pa)
temp_sf_pa_intra_flows <- gen_od_timing_PA_flows(temp_sf_pa_intra)
tmap_plot_pa(sf_BS_167_DIR_1_MPSZ, temp_sf_pa_intra_flows)st_drop_geometry(temp_sf_pa_intra_flows) %>% arrange(desc(PA_TRIPS)) ORIGIN_PA DESTIN_PA PA_TRIPS
1 NT OR 24.1619132
2 SB YS 20.4134534
3 NV OR 19.7632224
4 AM BS 18.8457835
5 DT BM 12.4242619
6 OR DT 10.9622361
7 MU DT 10.5904685
8 BS NV 8.9696601
9 YS BS 6.6820276
10 BS OR 6.2397735
11 NV NT 5.8671799
12 TP NV 5.6286715
13 TP OR 5.5041468
14 YS AM 4.7127183
15 OR MU 4.7117221
16 NV DT 4.0652153
17 NV MU 3.8329804
18 SB BS 3.5437788
19 OR BM 3.4402721
20 YS OR 3.4285714
21 AM OR 3.0476190
22 YS NV 2.4992320
23 BS TP 2.4221243
24 MU BM 2.0793847
25 NT MU 1.8674825
26 BS MU 1.8578755
27 NT DT 1.7648352
28 AM NV 1.7250384
29 AM MU 1.5238095
30 NV BM 1.3429465
31 SB OR 1.3333333
32 YS MU 1.2380952
33 SB AM 1.2104455
34 TP MU 1.1604396
35 YS TP 1.1059908
36 BS DT 1.0568081
37 SB BM 0.9523810
38 TP NT 0.9061419
39 YS BM 0.9047619
40 NT BM 0.7850717
41 SB NV 0.7803379
42 SB DT 0.7619048
43 AM DT 0.6666667
44 SB NT 0.6666667
45 SB MU 0.5714286
46 TP DT 0.5128683
47 BS BM 0.4910514
48 SB TP 0.4685100
49 YS DT 0.4285714
50 AM TP 0.4147465
51 AM NT 0.3809524
52 BS NT 0.3142857
53 AM BM 0.2857143
54 BM SB 0.2857143
55 YS NT 0.2857143
56 TP BM 0.1264972
57 BM DT 0.0000000
58 BM MU 0.0000000
59 BM OR 0.0000000
60 NT NV 0.0000000
61 OR NV 0.0000000
62 YS SB 0.0000000plot_trip_summary(temp_sf)temp_sf_pa_inter %>% arrange(desc(PA_TRIPS))# A tibble: 11 × 3
# Groups: ORIGIN_PA [11]
ORIGIN_PA DESTIN_PA PA_TRIPS
<chr> <chr> <dbl>
1 YS YS 24.1
2 BM BM 18.5
3 SB SB 8.08
4 BS BS 5.45
5 NV NV 3.82
6 OR OR 3.66
7 DT DT 2.41
8 AM AM 2.12
9 MU MU 1.81
10 TP TP 0.479
11 NT NT 0.172tmap_options(check.and.fix = TRUE)
temp_sf <- readRDS("data/167_OD_Analysis/sf_norm_12.rds")
temp_sf_pa <- gen_od_timing_PA(temp_sf)
temp_sf_pa_intra <- gen_od_timing_PA_intra(temp_sf_pa)
temp_sf_pa_inter <- gen_od_timing_PA_inter(temp_sf_pa)
temp_sf_pa_intra_flows <- gen_od_timing_PA_flows(temp_sf_pa_intra)
tmap_plot_pa(sf_BS_167_DIR_1_MPSZ, temp_sf_pa_intra_flows)st_drop_geometry(temp_sf_pa_intra_flows) %>% arrange(desc(PA_TRIPS)) ORIGIN_PA DESTIN_PA PA_TRIPS
1 NT OR 49.0513244
2 NV OR 18.3313521
3 AM BS 17.2722498
4 SB YS 15.5486300
5 DT BM 15.4027930
6 OR DT 13.0154106
7 MU DT 12.4042916
8 YS BS 8.7557604
9 OR MU 7.3955708
10 TP OR 6.4223488
11 TP NV 5.8910876
12 NV NT 5.5764864
13 YS AM 5.4940650
14 BS OR 4.9056443
15 BS NV 4.7520016
16 NV MU 4.7330169
17 OR BM 4.7135065
18 MU BM 3.5292438
19 YS OR 3.3809524
20 BS TP 2.7608115
21 SB BS 2.7050691
22 YS NV 2.5637481
23 NT MU 2.3324593
24 NV DT 2.2926797
25 AM OR 2.2380952
26 SB OR 1.7142857
27 NV BM 1.7006605
28 AM NV 1.6513057
29 SB AM 1.6175115
30 AM MU 1.5714286
31 YS TP 1.4976959
32 YS MU 1.4285714
33 BS DT 1.2579096
34 BS MU 1.2080586
35 YS BM 1.1428571
36 TP MU 1.1362637
37 SB BM 1.0476190
38 AM DT 0.9047619
39 NT BM 0.8713220
40 SB TP 0.8586790
41 BS BM 0.7283908
42 NT DT 0.7245421
43 AM TP 0.6682028
44 YS DT 0.6666667
45 TP NT 0.6613133
46 BS NT 0.6490842
47 SB MU 0.6190476
48 TP BM 0.5884148
49 BM SB 0.5238095
50 SB NV 0.4685100
51 AM NT 0.4285714
52 SB NT 0.4285714
53 SB DT 0.3333333
54 YS NT 0.3333333
55 TP DT 0.2930676
56 AM BM 0.2380952
57 BM DT 0.0000000
58 BM MU 0.0000000
59 BM OR 0.0000000plot_trip_summary(temp_sf)temp_sf_pa_inter %>% arrange(desc(PA_TRIPS))# A tibble: 11 × 3
# Groups: ORIGIN_PA [11]
ORIGIN_PA DESTIN_PA PA_TRIPS
<chr> <chr> <dbl>
1 YS YS 31.4
2 BM BM 21.5
3 SB SB 9.01
4 BS BS 5.92
5 DT DT 5.43
6 OR OR 4.80
7 AM AM 3.26
8 NV NV 2.83
9 MU MU 2.69
10 TP TP 0.655
11 NT NT 0.0916tmap_options(check.and.fix = TRUE)
temp_sf <- readRDS("data/167_OD_Analysis/sf_norm_18.rds")
temp_sf_pa <- gen_od_timing_PA(temp_sf)
temp_sf_pa_intra <- gen_od_timing_PA_intra(temp_sf_pa)
temp_sf_pa_inter <- gen_od_timing_PA_inter(temp_sf_pa)
temp_sf_pa_intra_flows <- gen_od_timing_PA_flows(temp_sf_pa_intra)
tmap_plot_pa(sf_BS_167_DIR_1_MPSZ, temp_sf_pa_intra_flows)st_drop_geometry(temp_sf_pa_intra_flows) %>% arrange(desc(PA_TRIPS)) ORIGIN_PA DESTIN_PA PA_TRIPS
1 DT BM 75.59138560
2 AM BS 41.61442548
3 SB YS 21.28153393
4 OR BM 13.56466784
5 MU BM 11.20804097
6 YS BS 9.39393939
7 YS AM 8.74280558
8 AM NV 4.04329004
9 MU DT 3.61982826
10 AM OR 2.57142857
11 SB BS 2.37662338
12 OR DT 2.28571429
13 NT BM 2.17230936
14 SB AM 1.87012987
15 YS NV 1.80952381
16 AM TP 1.79653680
17 YS OR 1.76190476
18 NV BM 1.69611190
19 YS TP 1.68831169
20 BM SB 1.00000000
21 AM MU 0.71428571
22 AM BM 0.66666667
23 SB BM 0.61904762
24 SB MU 0.61904762
25 TP BM 0.61105957
26 YS BM 0.57142857
27 BS BM 0.52527098
28 YS MU 0.42857143
29 SB NV 0.39826840
30 SB OR 0.38095238
31 SB TP 0.38095238
32 YS DT 0.38095238
33 NV DT 0.27712724
34 BS TP 0.27244790
35 BS NV 0.24776226
36 AM NT 0.23809524
37 BS DT 0.23809524
38 NT DT 0.23809524
39 BS MU 0.19047619
40 YS NT 0.19047619
41 BS OR 0.16356108
42 TP DT 0.11204482
43 AM DT 0.09523810
44 SB DT 0.09523810
45 SB NT 0.09523810
46 BS NT 0.04761905
47 BM DT 0.00000000
48 BM MU 0.00000000
49 BM OR 0.00000000
50 BM YS 0.00000000plot_trip_summary(temp_sf)temp_sf_pa_inter %>% arrange(desc(PA_TRIPS))# A tibble: 6 × 3
# Groups: ORIGIN_PA [6]
ORIGIN_PA DESTIN_PA PA_TRIPS
<chr> <chr> <dbl>
1 YS YS 35.5
2 BM BM 31.5
3 SB SB 12.6
4 AM AM 3.73
5 BS BS 3.06
6 DT DT 1.08tmap_options(check.and.fix = TRUE)
temp_sf <- readRDS("data/167_OD_Analysis/sf_norm_19.rds")
temp_sf_pa <- gen_od_timing_PA(temp_sf)
temp_sf_pa_intra <- gen_od_timing_PA_intra(temp_sf_pa)
temp_sf_pa_inter <- gen_od_timing_PA_inter(temp_sf_pa)
temp_sf_pa_intra_flows <- gen_od_timing_PA_flows(temp_sf_pa_intra)
tmap_plot_pa(sf_BS_167_DIR_1_MPSZ, temp_sf_pa_intra_flows)st_drop_geometry(temp_sf_pa_intra_flows) %>% arrange(desc(PA_TRIPS)) ORIGIN_PA DESTIN_PA PA_TRIPS
1 DT BM 42.34102384
2 AM BS 16.50320223
3 OR MU 16.35958338
4 SB YS 14.78329430
5 OR DT 13.90941043
6 OR BM 13.62984587
7 MU DT 12.56206927
8 NV OR 12.52243246
9 MU BM 11.73866239
10 NT OR 8.64239769
11 YS AM 7.67541029
12 NV NT 5.71267161
13 YS BS 5.26775956
14 BS NV 2.79977182
15 NV MU 2.78571429
16 NV BM 2.48885051
17 BS TP 2.45081777
18 TP NV 2.27305837
19 TP OR 1.99624364
20 NT BM 1.92516992
21 NT MU 1.70876649
22 SB BS 1.49960968
23 SB AM 1.39968774
24 YS TP 1.39890710
25 AM OR 1.38095238
26 NV DT 1.27142857
27 YS NV 1.25136612
28 AM TP 1.02732240
29 AM NV 1.02654176
30 BS OR 1.00185375
31 NT DT 0.66666667
32 SB NV 0.62919594
33 YS MU 0.61904762
34 BM SB 0.57142857
35 SB MU 0.52380952
36 YS OR 0.52380952
37 AM MU 0.47619048
38 SB TP 0.45667447
39 TP MU 0.40816327
40 BS DT 0.40238095
41 AM NT 0.38095238
42 TP NT 0.36068831
43 SB OR 0.33333333
44 BS MU 0.31746032
45 TP BM 0.21693160
46 BS NT 0.20634921
47 BS BM 0.15465587
48 AM BM 0.09523810
49 AM DT 0.09523810
50 SB BM 0.09523810
51 YS BM 0.09523810
52 YS NT 0.09523810
53 TP DT 0.09295270
54 SB DT 0.04761905
55 YS DT 0.04761905
56 BM MU 0.00000000
57 YS SB 0.00000000plot_trip_summary(temp_sf)temp_sf_pa_inter %>% arrange(desc(PA_TRIPS))# A tibble: 11 × 3
# Groups: ORIGIN_PA [11]
ORIGIN_PA DESTIN_PA PA_TRIPS
<chr> <chr> <dbl>
1 BM BM 26.6
2 YS YS 17.9
3 SB SB 10.6
4 DT DT 5.18
5 BS BS 4.00
6 OR OR 3.92
7 MU MU 2.97
8 NV NV 2.93
9 AM AM 0.861
10 TP TP 0.780
11 NT NT 0.0357tmap_options(check.and.fix = TRUE)
temp_sf <- readRDS("data/167_OD_Analysis/sf_norm_20.rds")
temp_sf_pa <- gen_od_timing_PA(temp_sf)
temp_sf_pa_intra <- gen_od_timing_PA_intra(temp_sf_pa)
temp_sf_pa_inter <- gen_od_timing_PA_inter(temp_sf_pa)
temp_sf_pa_intra_flows <- gen_od_timing_PA_flows(temp_sf_pa_intra)
tmap_plot_pa(sf_BS_167_DIR_1_MPSZ, temp_sf_pa_intra_flows)st_drop_geometry(temp_sf_pa_intra_flows) %>% arrange(desc(PA_TRIPS)) ORIGIN_PA DESTIN_PA PA_TRIPS
1 DT BM 30.12372519
2 OR BM 15.76603802
3 OR MU 11.73715526
4 MU BM 11.58357151
5 SB YS 11.25480094
6 OR DT 9.46546443
7 AM BS 7.60784789
8 MU DT 6.29761957
9 YS AM 4.12605279
10 NV OR 4.09979532
11 YS BS 3.47540984
12 NV NT 3.19162323
13 NT OR 3.17851951
14 BS TP 2.17639578
15 BS NV 2.01010078
16 TP NV 1.14594313
17 AM OR 1.00000000
18 NV BM 0.94252747
19 YS NV 0.93520687
20 NT BM 0.88231277
21 SB BS 0.87587822
22 NT MU 0.82334254
23 YS TP 0.80874317
24 NT DT 0.70975057
25 NV DT 0.70748299
26 SB AM 0.66354411
27 NV MU 0.65087764
28 BS OR 0.62183827
29 TP OR 0.60372222
30 AM NV 0.55581577
31 AM TP 0.54644809
32 YS OR 0.52380952
33 YS MU 0.47619048
34 SB BM 0.33333333
35 AM NT 0.28571429
36 BM SB 0.23809524
37 SB OR 0.23809524
38 YS BM 0.23809524
39 BS DT 0.23582766
40 SB TP 0.20062451
41 SB NV 0.17876659
42 TP BM 0.15494126
43 YS NT 0.14285714
44 BS MU 0.13605442
45 BS NT 0.13605442
46 TP NT 0.12301019
47 BS BM 0.11528822
48 AM BM 0.09523810
49 AM DT 0.09523810
50 SB DT 0.09523810
51 SB NT 0.09523810
52 TP DT 0.05719064
53 AM MU 0.04761905
54 TP MU 0.04535147
55 BM DT 0.00000000
56 BM MU 0.00000000
57 BM OR 0.00000000plot_trip_summary(temp_sf)temp_sf_pa_inter %>% arrange(desc(PA_TRIPS))# A tibble: 11 × 3
# Groups: ORIGIN_PA [11]
ORIGIN_PA DESTIN_PA PA_TRIPS
<chr> <chr> <dbl>
1 BM BM 19.6
2 YS YS 13.2
3 SB SB 4.44
4 DT DT 3.62
5 BS BS 2.20
6 OR OR 2.18
7 MU MU 1.73
8 NV NV 0.877
9 AM AM 0.502
10 TP TP 0.355
11 NT NT 0.0157Todo list:
Visualisation tmap for Subzone / bus stop see how to display data
EDA on trip - derivation on initial analysis (eg. focus on AM Peak Dir 1?)
- Sequential trial and error, might be worthwhile to check off peak trends as well - without data cannot determine which user group but maybe can guess?
K-means clustering on types of stops based on temporal data - each stop, pattern based on day type [probably seperate article]